Production Architecture
From AI Demo to Production - What Startups Get Wrong
Why AI prototypes fail in production and what to validate before you ship retrieval, agents, or inference to real users.
production startups ai
Most AI products fail between the demo and the first hundred real users - not because the model is wrong, but because production was never designed.
The demo trap
Demos optimize for the happy path. Production optimizes for variance: messy data, retries, latency spikes, and users who do not read your prompts.
This is the gap I spend most of my time in — you can read more about the production-readiness work I do with startups.
What to validate early
- Evals — define what “good” means before you ship. If retrieval is part of your stack, The Complete RAG Evaluation Pipeline walks through how to build that measurement layer.
- Observability - traces, logs, and quality signals on real traffic.
- Cost envelope - inference spend at 10× your expected volume.
- Fallbacks - what happens when the model fails or times out?
A practical next step
If you are past the notebook and need a credible path to production, get in touch - I help startups ship agentic and RAG systems that survive real users.